Training models for iot devices

ABSTRACT

A model training service of a provider network receives data from edge devices of a remote network. The model training service analyzes the received data. The model training service may also analyze global data from other edge devices of other remote networks. The model training service may then generate updates to local data processing models based on the analysis. The updates are configured to update the local data processing models at the edge devices of the remote network. The provider network deploys the updates to the remote network. The updates are then applied to the data processing models of the edge devices.

BACKGROUND

The Internet of Things (IoT) is a phrase given for the interconnectionof computing devices scattered around the globe within the existinginternet infrastructure. IoT devices may be embedded in a variety ofproducts, such as home appliances, manufacturing devices, printers,automobiles, thermostats, smart traffic lights, video cameras, etc.

In some cases, IoT devices make use of a connection with a hub device tobecome a part of a local network of devices. The hub device typically isa more powerful device capable of performing more computations and at afaster rate than IoT devices. For example, a house may have a hub devicethat forms a wireless connection to multiple different sensor IoTdevices, such as thermostats for measuring temperatures of differentrooms or motion sensors in different rooms. The hub device may receivetemperature values or motion data and transmit the temperature values ormotion data to one nor more other endpoints. If the hub device isconnected to the internet, then the values may be transmitted to aprovider network or a user device, such as the user's smart phone.

Some IoT devices are powerful enough to implement a relatively simpledata processing model to analyze data and generate a result, such as aprediction. However, the reliability of such a prediction may not be asgood as the reliability of a larger model running on a more powerfulcomputing device. For example, a large model implemented by a serviceprovider network or a server computer may use hundreds of millions ofparameters, whereas a model running on an IoT device may use only a fewhundred thousand. Moreover, the amount and the type of data received bya model at a given IoT device may change over time. For example, a videocamera may capture images of novel object or under novel lightingconditions that is was not explicitly trained on. Therefore, even thoughthe model on the camera may have been initially trained based onadequate training data, the model may lose accuracy and become lessuseful over time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for implementing a split prediction based ona local model of a hub device and a provider network, according to someembodiments.

FIG. 2 is a block diagram of an example data hub device that implementsa local model and a result manager, according to some embodiments.

FIG. 3 is a flow diagram that illustrates implementing a splitprediction based on a local model of a hub device and a providernetwork, according to some embodiments.

FIG. 4 illustrates a system for implementing a split prediction based ona local model of an edge device and a provider network, according tosome embodiments.

FIG. 5A is a flow diagram that illustrates implementing a splitprediction based on a local model of an edge device and a providernetwork, according to some embodiments.

FIG. 5B is a flow diagram that illustrates a data processing service ofa provider network that generates and transmits a result to a computingdevice of a remote network, according to some embodiments.

FIG. 6 illustrates a system for updating models for edge devices by aprovider network, according to some embodiments.

FIG. 7 is a block diagram of an edge device that implements a localmodel, according to some embodiments.

FIG. 8A is a flow diagram that illustrates updating models for edgedevices by a provider network, according to some embodiments.

FIG. 8B is a flow diagram that illustrates updating a local model of anedge device, according to some embodiments.

FIG. 9 illustrates a system for updating models for edge devices by ahub device, according to some embodiments.

FIG. 10 is a flow diagram that illustrates updating models for edgedevices by a hub device, according to some embodiments.

FIG. 11 is a block diagram of an edge device that implements multiplelocal models, according to some embodiments.

FIG. 12 is a flow diagram that illustrates selecting a local model toprocess data at an edge device, according to some embodiments.

FIG. 13 illustrates a system for implementing model tiering acrossmultiple devices of a network, according to some embodiments.

FIG. 14 is a block diagram of an edge device that implements a model anda tier manager, according to some embodiments.

FIG. 15 is a flow diagram that illustrates implementing model tieringacross multiple devices of a network, according to some embodiments.

FIG. 16 is a flow diagram that illustrates implementing model tieringacross multiple devices of a network to generate a prediction, accordingto some embodiments.

FIG. 17 illustrates a system for implementing model tiering by sendingdifferent portions of data to different models, according to someembodiments.

FIG. 18 is a flow diagram that illustrates implementing model tiering bysending different portions of data to different models, according tosome embodiments.

FIG. 19 is a block diagram illustrating an example computer system thatimplements some or all of the techniques described herein, according tosome embodiments.

While embodiments are described herein by way of example for severalembodiments and illustrative drawings, those skilled in the art willrecognize that embodiments are not limited to the embodiments ordrawings described. It should be understood, that the drawings anddetailed description thereto are not intended to limit embodiments tothe particular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope as defined by the appended claims. The headings usedherein are for organizational purposes only and are not meant to be usedto limit the scope of the description or the claims. As used throughoutthis application, the word “may” is used in a permissive sense (i.e.,meaning having the potential to), rather than the mandatory sense (i.e.,meaning must). Similarly, the words “include,” “including,” and“includes” mean including, but not limited to.

DETAILED DESCRIPTION OF EMBODIMENTS

The systems and methods described herein implement techniques forconfiguring local networks of internet-connectable devices (e.g., IoTdevices) to implement data processing models to rapidly generate localresults (e.g., local predictions), while also taking advantage oflarger, more accurate data processing models running on more powerfuldevices (e.g., servers of a provider network) that generate moreaccurate results based on the same data.

In embodiments, a remote provider network and a local network device(e.g., hub device or edge device) may be used to generate a splitprediction. For example, a hub device of a local network may receivedata from sensors and process the data using a local model (e.g., dataprocessing model) to generate a local prediction. The sensor data mayalso be transmitted to a remote provider network, which returns anotherprediction. If the returned prediction from the provider network is moreaccurate, then the local prediction may be corrected by the returnedprediction.

In some embodiments, a provider network and/or a hub device may updatelocal data models of edge devices based on data collected by the edgedevices. For example, a provider network and/or a hub device mayperiodically receive data from edge devices and generate new updates tolocal models based on the new data. The provider network and/or a hubdevice may then deploy the updates to the respective edge devices. Inembodiments, entirely new versions of the local models are deployed toreplace current models of the respective edge devices.

In embodiments, multiple models may be implemented across multiplerespective edge devices (e.g., tier devices) of a network. Some tierdevices may implement larger, more accurate models than other tierdevices. When a tier device receives data (e.g., from a sensor), thetier device may decide whether to process the data using a local modelof the tier device or to send the data on to another tier device thathas a larger, more accurate model for processing the data.

As used herein, a model may be any data processing model suitable forprocessing input data to generate one or more results. For example, amodel may include a neural network, deep neural network, static ordynamic neural network, a memory network, and/or any other modelarchitecture suitable for processing the collected data and generatingone or more results/predictions. Thus, in embodiments, a model mayinclude any computational model, machine learning model, or artificialintelligence model suitable for processing data and generating one ormore results. Moreover, in some embodiments, different devices (e.g.,edge devices, tier devices, hub devices, servers, etc.) may includedifferent model architectures and/or be trained based on the same ordifferent training data.

In various embodiments, any suitable security communication protocolsmay be used to protect data that is being sent between any devices(e.g., edge devices, tier devices, hub devices, servers, etc.) andbetween devices of different networks (e.g., local networks, cellularnetworks, provider networks). For example, data may be encrypted usingSSL (secure socket layer), TLS (transport layer security), HTTPS (securehypertext transfer protocol), and/or any other suitable network securityprotocol.

In embodiments, any of the data collected by a data collector (e.g.,sensor) may be converted into training data and used to train modelsand/or generate updates to models. Thus, analyzing data and/orgenerating a model based on the analysis may include creating trainingdata and creating modifications and/or updates to the model based on thetraining data.

The term “prediction” is used herein for various examples. However, invarious embodiments and in the figures, any other suitable result (e.g.,non-prediction result) may be substituted for a prediction. A result maybe an identification based on data received from a sensor. For example,a result may be an identification of a particular person, animal, orobject based on analysis of image data using an image classifier. Insome embodiments, the word “prediction” may be used instead of “result,”and vice versa.

Moreover, a prediction may be a result that identifies a predictedoutcome or a predicted future state for an object, environment, person,animal, or anything else that may change state or location over time.For example, a prediction may be a temperature of an area or location aday from the current time. As another example, a prediction may be alocation of an object 5 seconds from the current time. In embodiments,predictions may be bounding boxes around certain types of objects in animage or may be generated responses of a system to one or moreenvironmental conditions and/or events.

FIG. 1 illustrates a system for implementing a split prediction based ona local model of a hub device and a provider network, according to someembodiments. A hub device 100, a provider network 102, local network108, edge devices 106, and any other components depicted in FIG. 1 maybe the same as or include one or more of the same components as the hubdevice, the provider network, local network, edge devices, and any othercomponents depicted in any of FIGS. 2-18, in embodiments. Inembodiments, the hub device, provider network, local network, edgedevices, tier devices, and other components depicted in any one of FIGS.1-16 may be the same component as (e.g., same type of component) orinclude one or more of the same components as a hub device, providernetwork, local network, edge devices, tier devices, and other componentsdepicted in any other one of FIGS. 1-16.

Although FIGS. 1-16 may describe a single hub device of a local network,in various embodiments any number of hub devices may be used instead ofa single hub device. For example, in some embodiments, multiple hubdevices may be used as redundant hub devices to add fault-tolerance tothe system. If one or more hub devices fail, a remaining one or more hubdevices may continue to operate as described. Thus, in embodiments, ahub device may synchronize state with one or more other hub devices(e.g., at periodic intervals or upon detecting an event such as additionof a new hub device or failure of a hub device).

In some embodiments, two or more hub devices may collectively performthe various described operations instead of just one hub device. Thus,two or more hub devices may each perform a particular portion of thedescribed operations (e.g., distributed processing). In otherembodiments, a combination of redundant and distributed processing maybe implemented via two or more hub devices. In embodiments, the hubdevice may represent any compute devices described herein, including anedge device or tier device.

In the depicted embodiment, the hub device 100 includes a local model108 that may receive data from one or more edge devices 106 and processthe received data. The local model 108 may include software and/orhardware that performs one or more operations on received data. Inembodiments, the one or more operations may include analyzing the data,modifying the data based on the analyzing, and/or generating a resultbased on the analyzing (e.g., prediction, new data, one or morecommands, or some other result). For example, the result may be a localprediction 110. In embodiments, the local prediction may be received bya result manager 112.

In some embodiments, the hub device 100 (e.g., the result manager 112)may also transmit the data received from one or more edge devices 106 tothe provider network 102. The data may be transmitted via a wide-areanetwork 114 (e.g., the internet). Thus, the provider network 102 may beconsidered a remote provider network and may be in another physicallocation than the hub devices, such as another city, state, or country.In embodiments, the provider network 102 includes a data processingservice 116 that includes a model 118 that receives the data from thehub device 112 and processes the received data.

Similar to the local model 108, the model 118 may include softwareand/or hardware that performs one or more operations on received data.In embodiments, the one or more operations may include analyzing thedata, modifying the data based on the analyzing, and/or generating aresult based on the analyzing (e.g., prediction, new data, one or morecommands, or some other result). For example, the result may be aprediction 120. The data processing service 116 may then transmit theprediction 120 to the hub device 100 via the network 114.

In embodiments, the result manager 112 receives the prediction 120. Theresult manager 112 may then determine whether to correct the localprediction 110 based on the prediction 120 and/or the local prediction110. If the result manager 112 determines not to correct the localprediction 110, then the result manager 112 generates a response basedon the local prediction 110. For example, the result manager 112 maygenerate a command or other message that causes the hub device 100 toperform an action. In embodiments, the result manager 112 may send thecommand or message to an endpoint, such as another edge device of thelocal network 104 or another computing device outside of the localnetwork 104.

If the result manager 112 determines to correct the local prediction110, then the result manager 112 corrects the local prediction 110 basedon the prediction 120. For example, the result manager 112 may modifythe local prediction 110 based on the prediction 120. In someembodiments, to correct the local prediction 110, the result manager 112replaces the local prediction 110 with the prediction 120.

In embodiments, the result manager 112 then generates a response basedon the corrected prediction. For example, the result manager 112 maygenerate a command or other message that causes the hub device 100 toperform an action. In embodiments, the result manager 112 may send thecommand or message to an endpoint, such as another edge device of thelocal network 104 or another computing device outside of the localnetwork 104.

In some embodiments, to determine whether to correct the localprediction 110 based on the prediction 120 and/or the local prediction110, the result manager 112 may first determine a confidence level forthe local prediction 110 and a confidence level for the prediction 120.If the confidence level for the local prediction 110 is lower than theconfidence level for the prediction 120, then the result manager 112 maydetermine to correct the local result. In embodiments, if the confidencelevel for the local prediction 110 is equal to or greater than theconfidence level for the prediction 120, then the result manager 112 maydetermine not to correct the local result.

In some embodiments, the result manager 112 may determine whether aconfidence level for the local prediction 110 is above a thresholdlevel. If so, then the result manager 112 may generate an initialresponse based on the local prediction 110 before receiving theprediction 120 from the provider network 102 or before determiningwhether to correct the local prediction 110 based on the prediction 120and/or the local prediction 110. In some embodiments, the result manager112 determines a confidence level for the prediction 120 and if theconfidence level for the local prediction 110 is lower than theconfidence level for the prediction 120, then the result manager 112determines to correct the local result. In such embodiments, theresponse based on the corrected result may be different than the initialresponse.

Thus, in embodiments, the result manager 112 may use the localprediction 110 to generate a response, without waiting to receive theprediction 120. Moreover, the result manager 112 may not correct thelocal prediction 110 and may not use the prediction 120. For example,after the result manager 112 uses the local prediction 110 to generate aresponse, then the result manager 112 may ignore the prediction 120 thatis received at a later time. In some embodiments, the result manager mayuse the received prediction 120 to update and/or modify the local model108 to improve the accuracy of the local model 108 in making predictionsbased on additional data received at a later time.

In embodiments, the hub device 100 and the edge devices 106 may each beconnected to the same local network 104. In various embodiments, one ormore of the hub device 100 and the edge devices 106 may be capable ofsending data to any other device of the local network 104 via wireand/or via wireless transmission.

As shown in the depicted embodiment, each edge device 110 includes adata collector 122. The data collector 122 may include any suitabledevice for collecting and/or generating data and sending the data to thehub device 100. For example, the data collector 122 may be anenvironmental sensor device that detects one or more environmentalconditions (e.g., temperature, humidity, etc.) and generates and/orcollects environmental data based on the detected environmentalconditions. Thus, the generated data may indicate one or moreenvironmental conditions of the edge device 106 or the local network104.

In embodiments, the data collector 122 may be a sensor or other devicethat detects performance and/or other operational aspects of the network(e.g., network bandwidth or traffic, power consumption of a data sourcedevice, etc.) and generates data based on the detected performance.Thus, the generated data may indicate performance or other operationalaspects of the local network and/or the edge device 106. In embodiments,the generated data may be sent to the hub device 100, which may also besent to the provider network 102.

In some embodiments, the data collected by the data collector 122 is atime-series data set and the data collector 122 performs one or moreoperations on the time-series data set to generate the data to betransmitted to the hub device 100. For example, the data collector 122may calculate an average value based on two or more data values of thecollected data in order to generate the data to be transmitted.

In embodiments, the provider network 102 may also include a deploymentservice 124 that generates and/or deploys the local model 108 and/or theresult manager 112 to the hub device 100. In some embodiments, thedeployment service 124 may generate the local model 108 and/or theresult manager 112 based on data that describes one or more performancespecifications and/or capabilities of the hub device 100.

By implementing a split prediction as described above, an initialresponse based on a local prediction may be provided in a shorter amountof time than if the hub device waited for a prediction to be receivedthe provider network. Moreover, by providing the ability to correct thelocal prediction, the hub device may leverage the more powerful model ofthe provider network to alter or change the response generated by thehub device. Therefore, the techniques above allow for a faster responsebased on the local prediction, while also taking advantage of moreaccurate predictions received from the provider network that may be usedto correct the local prediction and to make appropriate adjustments tothe response.

In embodiments, an edge device may be any type of computing device thatcollects data from the environment via one or more sensors. For example,an edge device may be a microphone, camera, temperature sensing device,humidity detector, vibration detector, smoke detector, motion sensor,etc. In embodiments, an edge device may be any type of intelligent IoTdevice capable of communicating with other computing devices.

FIG. 2 is a block diagram of an example hub device that implements alocal model and a result manager, according to some embodiments. In thedepicted embodiment, the hub device 100 includes processor 200, a memory202, a battery 204, and a network interface 206. The memory 202 includesa result manager 112 and a local model 108. The processor 200 may be anysuitable processor, such as a graphics processing unit (GPU), GPUaccelerator, Field Programmable Gate Array (FPGA), Digital SignalProcessor (DSP), DSP accelerator, or application-specific integratedcircuit (ASIC). In embodiments, the ASIC may also include the memory 202and/or any other component of FIG. 2.

In some embodiments, the memory 202 includes executable instructions andthe processor 208 executes the instructions in order to implement theresult manager 112 and/or the local model 108. In embodiments, thenetwork interface 206 communicatively couples the hub device 100 to thelocal network. Thus, the hub device 100 may transmit data to and receivedata from edge devices via the network interface 206. In embodiments,the network interface 206 may transmit data via a wired or wirelessinterface.

In some embodiments, the hub device and one or more of its components(e.g., processor and memory) may be relatively lightweight and smallercompared to components (e.g., processor and memory) used by the providernetwork 102 to implement the data processing service 116 and/or themodel 118. For example, the size of one or more memories and/or one ormore processors used by one or more servers of the provider network 102to implement the data processing service 116 and/or the model 118 may beat least an order of magnitude larger than the size of the memory and/orthe processor used by the hub device 100. Therefore, in embodiments, themodel 118 of the data processing service 116 may produce more accurateresults (e.g., predictions) with higher confidence levels, and at afaster rate than the local model 108.

FIG. 3 is a flow diagram that illustrates implementing a splitprediction based on a local model of a hub device and a providernetwork, according to some embodiments. In embodiments, the hub devicemay be an edge device, tier device, or any other type of computingdevice suitable for performing the described functions. At block 302, ahub device receives data from one or more edge devices of a localnetwork. At block 304, the hub device performs one or more operations onthe data using a local model of the hub device. At block 306, the hubdevice also sends the data to a remote provider network.

In embodiments, at least some of the steps of block 304 and/or 308 mayoccur at approximately the same time or concurrently with block 306and/or block 310. In embodiments, in response to receiving the data fromthe one or more edge devices of the local network, the hub device sendsthe data to the remote provider and sends the data to the local model tobegin performing the one or more operations on the data using the localmodel.

At block 308, the hub device and/or the local model generates a localprediction in response to performing the one or more operations. Atblock 310, the hub device receives a prediction from the remote providernetwork, where the prediction is based on the data that was sent to theremote provider network. In embodiments, the hub device sends the localprediction to the result manager in response to the generating of thelocal prediction and/or the hub device sends the prediction from theprovider network to the result manager in response to the receiving theprediction from the provider network. Therefore, the result manager mayreceive the local prediction or the prediction first, depending on wheneach is ready first.

At block 312, the hub device (e.g., result manager) determines whetherto correct the local prediction to generate a corrected prediction. Inembodiments, the result manager determines, based on the received resultand the local result, whether to correct the local result to generate acorrected result. The result manager may then generate a response basedon the determination. For example, the result manager may determine aconfidence level for the local result and determine a confidence levelfor the result. If the confidence level for the local result is lowerthan the confidence level for the result, then the result manager maycorrect the local result to generate the corrected result.

In some embodiments, the received result includes an update for the dataprocessing model of the hub device. Thus, the hub device may update thelocal model using the update received from the provider network.

If the hub device determines not to correct the local prediction, thenat block 314, the hub device generates a response based on the localprediction. If the hub device determines to correct the localprediction, then at block 316, the hub device corrects the localprediction. At block 318, the hub device generates a response based onthe corrected prediction.

FIG. 4 illustrates a system for implementing a split prediction based ona local model of an edge device and a provider network, according tosome embodiments. The edge device 400 includes a result manager 112, alocal model 108, and one or more data collectors 122. The providernetwork 102 includes the data processing service 116, which includes themodel 118. The provider network 102 also includes the deployment service124.

In the depicted embodiment, the edge device 400 includes a local model108 that may receive data from one or more data collectors 122 (e.g.,sensors) and process the received data. The local model 108 may includesoftware and/or hardware that performs one or more operations onreceived data. In embodiments, the one or more operations may includeanalyzing the data, modifying the data based on the analyzing, and/orgenerating a result based on the analyzing (e.g., prediction, new data,one or more commands, or some other result). For example, the result maybe a local prediction 110. In embodiments, the local prediction 110 maybe received by a result manager 112.

In some embodiments, the edge device 400 (e.g., the result manager 112)may also transmit the data received from the one or more data collectors122 to the provider network 102. The data may be transmitted via awide-area network 114 (e.g., the internet). Thus, the provider network102 may be considered a remote provider network and may be in anotherphysical location than the edge device, such as another city, state, orcountry. In embodiments, the data processing service 116 and/or themodel 118 receives the data sent from the hub device 112 and processesthe received data.

As described above, the model 118 may include software and/or hardwarethat performs one or more operations on received data. The one or moreoperations may include analyzing the data, modifying the data based onthe analyzing, and/or generating a result based on the analyzing (e.g.,prediction 120, new data, one or more commands, or some other result).The data processing service 116 may then transmit the prediction 120 tothe edge device 400 via the network 114.

In embodiments, the result manager 112 receives the prediction 120. Theresult manager 112 may then determine whether to correct the localprediction 110 based on the prediction 120 and/or the local prediction110. If the result manager 112 determines not to correct the localprediction 110, then the result manager 112 generates a response basedon the local prediction 110. For example, the result manager 112 maygenerate a command or other message that causes the edge device 400 toperform an action. In embodiments, the result manager 112 may send thecommand or message to an endpoint, such as another edge device of thelocal network 104, another computing device outside of the local network104, or another endpoint within the edge device 400.

If the result manager 112 determines to correct the local prediction110, then the result manager 112 corrects the local prediction 110 basedon the prediction 120. For example, the result manager 112 may modifythe local prediction 110 based on the prediction 120. In someembodiments, to correct the local prediction 110, the result manager 112replaces the local prediction 110 with the prediction 120.

In embodiments, the result manager 112 then generates a response basedon the corrected prediction. For example, the result manager 112 maygenerate a command or other message that causes the edge device 400 toperform an action. In embodiments, the result manager 112 may send thecommand or message to an endpoint, such as another edge device of thelocal network 104, another computing device outside of the local network104, or an endpoint within the edge device 400.

In some embodiments, to determine whether to correct the localprediction 110 based on the prediction 120 and/or the local prediction110, the result manager 112 may first determine a confidence level forthe local prediction 110 and a confidence level for the prediction 120.If the confidence level for the local prediction 110 is lower than theconfidence level for the prediction 120, then the result manager 112 maydetermine to correct the local result. In embodiments, if the confidencelevel for the local prediction 110 is equal to or greater than theconfidence level for the prediction 120, then the result manager 112 maydetermine not to correct the local result.

In some embodiments, the result manager 112 may determine whether aconfidence level for the local prediction 110 is above a thresholdlevel. If so, then the result manager 112 may generate an initialresponse based on the local prediction 110 before receiving theprediction 120 from the provider network 102 or before determiningwhether to correct the local prediction 110 based on the prediction 120and/or the local prediction 110. In some embodiments, the result manager112 determines a confidence level for the prediction 120 and if theconfidence level for the local prediction 110 is lower than theconfidence level for the prediction 120, then the result manager 112determines to correct the local result. In such embodiments, theresponse based on the corrected result may be different than the initialresponse.

In embodiments, the edge device 400 and one or more other edge devices106 may each be connected to the same local network. In someembodiments, at least some of the data received by the data model isreceived from the one or more other edge devices 106. In variousembodiments, one or more of the edge device 400 and the edge devices 106may be capable of sending data to any other device of the local network104 via wire and/or via wireless transmission.

In embodiments, a data collector 122 may include any suitable device forcollecting and/or generating data. For example, a data collector 122 maybe an environmental sensor device that detects one or more environmentalconditions (e.g., temperature, humidity, etc.) and generates and/orcollects environmental data based on the detected environmentalconditions. Thus, the generated data may indicate one or moreenvironmental conditions of the edge device 106 or the local network104.

In embodiments, a data collector 122 may be a sensor or other devicethat detects performance and/or other operational aspects of the edgedevice 400 (e.g., network bandwidth or traffic, power consumption of anedge device, etc.) and generates data based on the detected performance.Thus, the generated data may indicate performance or other operationalaspects of the edge device 400. In embodiments, the generated data maybe sent to the provider network 102.

In some embodiments, the data collected by a data collector 122 is atime-series data set and the data collector 122 performs one or moreoperations on the time-series data set to generate the data to be sentto the local model 108. For example, the data collector 122 maycalculate an average value based on two or more data values of thecollected data in order to generate the data to be sent.

In embodiments, the provider network 102 may also include a deploymentservice 124 that generates and/or deploys the local model 108 and/or theresult manager 112 to the edge device 400. In some embodiments, thedeployment service 124 may generate the local model 108 and/or theresult manager 112 based on data that describes one or more performancespecifications and/or capabilities of the edge device 400.

By implementing a split prediction as described above, an initialresponse based on a local prediction may be provided in a shorter amountof time than if the edge device waited for a prediction to be receivedthe provider network. Moreover, by providing the ability to correct thelocal prediction, the edge device may leverage the more powerful model(e.g., using a larger number of parameters and/or compute power) of theprovider network to alter or change the response generated by the edgedevice. Therefore, the techniques above allow for a faster responsebased on the local prediction, while also taking advantage of moreaccurate predictions received from the provider network that may be usedto correct the local prediction and to make appropriate adjustments tothe response.

FIG. 5A is a flow diagram that illustrates implementing a splitprediction based on a local model of an edge device and a providernetwork, according to some embodiments. At block 502, an edge devicereceives data from one or more data collectors. At block 504, the edgedevice performs one or more operations on the data using a local modelof the edge device. At block 506, the edge device also sends the data toa remote provider network.

In embodiments, at least some of the steps of block 504 and/or 508 mayoccur at approximately the same time or concurrently with block 506and/or block 510. In embodiments, in response to receiving the data fromthe one or more data collectors, the edge device sends the data to theremote provider and sends the data to the local model to beginperforming the one or more operations on the data using the local model.

At block 508, the edge device and/or the local model generates a localprediction in response to performing the one or more operations. Atblock 510, the edge device receives a prediction from the remoteprovider network, where the prediction is based on the data that wassent to the remote provider network. In embodiments, the edge devicesends the local prediction to the result manager in response to thegenerating of the local prediction and/or the edge device sends theprediction from the provider network to the result manager in responseto the receiving the prediction from the provider network. Therefore,the result manager may receive the local prediction or the predictionfirst, depending on when each is ready first.

At block 512, the edge device (e.g., result manager) determines whetherto correct the local prediction to generate a corrected prediction. Ifthe edge device determines not to correct the local prediction, then atblock 514, the edge device generates a response based on the localprediction. If the edge device determines to correct the localprediction, then at block 516, the edge device corrects the localprediction. At block 518, the edge device generates a response based onthe corrected prediction.

FIG. 5B is a flow diagram that illustrates a data processing service ofa provider network that generates and transmits a result to a computingdevice of a remote network, according to some embodiments. Inembodiments, the computing device may be an edge device, hub device,tier device, or any other computing device suitable for performing thedescribed functions.

At block 520, the data processing service obtains data from a computingdevice of a remote network (e.g., edge device 400 or hub device 100). Inembodiments, the data may be collected from one or more data collectors(e.g., sensors) of the computing device. In embodiments, an edge device400 may be considered a computing device of a remote network, eventhough the edge device 400 may be the only edge device of the remotenetwork.

At block 522, the data processing service obtains a remote result from aremote data processing model of the computing device, wherein the remoteresult is based on the data. Thus, the data processing model of thecomputing device may have generated the remote result and transmitted itto the data processing service.

At block 524, the data processing service performs, by a data processingmodel of the data processing service, one or more operations on the datato generate a result. At block 526, the data processing service modifiesthe result. For example, the data processing service may modify theresult based on one or more differences between the remote result andthe result. In some embodiments, the data processing service may modifythe result based on a difference between a confidence level for theremote result and a confidence level for the result.

In embodiments, the result may be modified by determining one or moredelta values based at least on differences between the remote result andthe result and then modifying the result by replacing the result withthe one or more delta values (e.g., with a gradient or incrementalchanges). In embodiments, the size of the one or more delta valuestransmitted to the computing device of the remote network is smallerthan a size of the result that was replaced by the one or more deltavalues. Therefore, less bandwidth is used when sending the result to thecomputing device of the remote network, optimizing the transmission ofthe result.

In some embodiments, the data processing service may also generate anupdate for the remote data processing model of the computing devicebased on the remote result and the result and then transmit the updatefor the remote data processing model to the computing device of theremote network. The computing device may then update the remote dataprocessing model using the update. For example, the data processingservice may determine one or more changes that need to be made toincrease accuracy of the remote data processing model, based on one ormore differences between the remote result and the result. Only thechanges to the remote data processing model are sent, instead of a newremote data processing model. Thus, less bandwidth is used to update theremote data processing model.

FIG. 6 illustrates a system for updating models for edge devices by aprovider network, according to some embodiments. In the depictedembodiment, the edge devices 600 are connected to a local network 104and include a local model 602 and a data collector 122. The providernetwork 102 includes a model training service 604 that generates one ormore model updates 606.

One or more of the edge devices may collect data from a respective datacollector 122 and send the data to the provider network via the localnetwork 104 and the network 114. For example, the edge device 600 a maycollect and send the data 608 a to the provider network 102 and the edgedevice 600 n may collect and send the data 608 n to the provider network102.

In embodiments, the model training service 604 of the provider network102 may receive the data 608 from one or more of the edge devices 600.For example, the model training service 604 may receive the data 608 aand data 608 n. The model training service 604 may then analyze thereceived data. The model training service 604 may then generate a localmodel update 606 a to the local model 602 a based on the analysis of thedata 608 a and generate a local model update 606 n to the local model602 n based on the analysis of the data 608 n.

In embodiments, the local model update 606 a is configured to update thelocal model 602 a and the local model update 606 n is configured toupdate the local model 602 n. For example, each of the local modelupdates 606 a, 606 n may include computer-executable instructions and/orother data that is configured be loaded into memory and to update thecorresponding local models 602 a, 602 n when executed by a processor ofthe respective edge device 600 a, 600 n.

In response to the generating of the local model updates 606 a, 606 n,the model training service 604 may deploy the local model updates 606 a,606 n to the local network 104 and/or deploy the local model updates 606a, 606 n to the respective edge device 600 a, 600 n. Although the aboveexample includes only two model updates, any other number of local modelupdates 606 may be generated and deployed by the model training service604 to respective edge devices 600 based on received data, in the mannerdescribed above.

In embodiments, one or more of the model updates may include one or morenew versions of the respective local models configured to replace therespective local models. Thus, instead of just modifying an existinglocal model, in some cases it is replaced by a different model that is amore recent version.

In some embodiments, one or more of the edge devices 600 may include amodel trainer 612 that generates a local model update 614 and appliesthe local model update 614 to update the local model 602. For example,an edge device 600 may receive data from the data collector 122 andanalyze the data. The edge device 600 may then generate an update 614 tothe local model 602 based on the analysis of the data, wherein theupdate 614 is configured to update the local model 602. The modeltrainer 612 may then apply the update 614 to the local model 602.

In embodiments, the edge device 600 may also send the data received fromthe data collector 122 to the model training service 604. The edgedevice 600 may then receive another local model update 606 from themodel training service, wherein the local model update 606 is based onthe data received from the data collector 122. The edge device 600and/or the model trainer 612 may then apply the local model update 606to the local model 602.

In some embodiments, the local model update 606 is a new version of thelocal model, and the edge device 600 and/or the model trainer 612replaces the local model 602 with the new version of the local model. Inembodiments, the edge device 600 and/or the model trainer 612 receivesinitial data from the data collector 122 and analyzes the initial datato initially generate the local model 602. In other embodiments, theedge device 600 initially receives the local model 602 from the modeltraining service of the provider network 102.

In embodiments, the model trainer 612 receives global data from themodel training service 604, wherein the global data is based on datacollected by one or more other edge devices of one or more other remoteedge devices 610. The model trainer 612 may then analyze the global dataand generate the update 614 based on the analysis of the global data.

In some embodiments, the local model update 606 received from the modeltraining service is based on both the data received from the datacollector 122 and global data collected by one or more other edgedevices of one or more other edge devices 610. For example, the modeltraining service 604 may generate the local model update 606 based onthe data received from the data collector 122 and global data collectedby one or more other edge devices of one or more other edge devices 610.

In embodiments, the model trainer 612 and/or the edge device 600receives, on a periodic basis, an additional local model update 606 fromthe model training service 604 of the provider network 102, wherein theadditional local model update 606 is based at least on additional datacollected by the data collector 122 and sent to the model trainingservice 604 of the provider network 102 on a periodic basis. Forexample, once a day the model training service 604 may collectadditional data using the data collector 122 and send the additionaldata to the model training service 604 of the provider network 102. Inresponse the model training service 604 generates an additional localmodel update 606 and sends it to the model trainer 612 and/or the edgedevice 600.

By using the above techniques, an edge device may update the local modelby applying the update 614 to improve accuracy of the local model. Then,another local model update 606 may be received from the model trainingservice and the edge device may apply the local model update 606 toimprove the accuracy of the local model even more. This is possiblebecause the model training service 604 may use a larger model and/or useadditional training data to generate the local model update 606 that isnot available to the edge device.

As discussed above, the model training service may initially create anddeploy a given local model to the remote network and/or edge device. Forexample, the model training service may generate one or more of thelocal models for one or more of the respective edge devices of theremote network and then deploy the one or more local models to theremote network. In embodiments, the model training service may generatea given local model based on topology data or any other data receivedfrom a corresponding edge device that will be implementing the localmodel.

In embodiments, the model training service may receive global data fromone or more other edge devices of one or more other edge devices 610,analyze the global data, and generate the one or more updates to localmodels based on the analysis of the received data and the analysis ofthe global data. The updates may then be deployed to respective edgedevices. For example, images captured by cameras from many differenthomes and/or businesses may be used to enhance training data that isused for generation of local models.

In some embodiments, the model training service may apply differentweights to at least some of the received data and at least some of theglobal data during the analysis of the received data and the analysis ofthe global data. For example, a higher weight may be applied to thereceived data from the local network than to the global data during theanalysis of the received data and the analysis of the global data. Thus,an amount of data received by the model training service from the localnetwork may have a larger impact on the training of a local model thanthe same amount of global data received by the model training service.

Conversely, in some embodiments, a higher weight may be applied to theglobal data than to the received data from the local network during theanalysis of the received data and the analysis of the global data. Thus,an amount of global data received by the model training service may havea larger impact on the training of a local model than the same amount ofdata received by the model training service from the local network.

In embodiments, analysis of data from two or more different edge devicesmay be used to generate an update for a local model of a particular edgedevice. Thus, one local model of a particular edge device may be updatedand/or trained not only based on data from the particular edge device,but also based on data from one or more other edge devices of the samelocal network and/or other networks. In other words, the generating ofat least one of the updates to a particular local data processing modelmay be based on analysis of a portion of the data received by the modeltraining service from two or more different edge devices.

In some embodiments, the model training service may generate and deployone or more additional updates for one or more of the respective localdata processing models to one or more respective edge devices on aperiodic basis and/or in response to a triggering event. For example,updates may be generated based on received data and deployed to edgedevices on an hourly, daily, or weekly basis, or in response to themodel training service receiving a command to update one or more of thelocal models of respective edge devices.

FIG. 7 is a block diagram of an edge device that implements a localmodel, according to some embodiments. In the depicted embodiment, theedge device 600 includes processor 200, a memory 202, a battery 204, anda network interface 206. The memory 202 includes a local model 602.

In some embodiments, the memory 202 includes executable instructions andthe processor 208 executes the instructions in order to implement thelocal model 108. In embodiments, the network interface 206communicatively couples the edge device 600 to the local network. Thus,the edge device 600 transmits data to the hub device 100 via the networkinterface 206. In embodiments, the network interface 206 may transmitdata via a wired or wireless interface.

In some embodiments, the edge device and one or more of its components(e.g., processor and memory) may be relatively lightweight and smallercompared to components (e.g., processor and memory) used by the providernetwork to implement the model training service. For example, the sizeof one or more memories and/or one or more processors used by one ormore servers of the provider network to implement the model trainingservice may be at least an order of magnitude larger than the size ofthe memory and/or the processor used by the edge device.

FIG. 8A is a flow diagram that illustrates updating models for edgedevices by a provider network, according to some embodiments. At block802, a model training service of a provider network receives data fromone or more respective edge devices of a remote network. At block 804,the model training service analyzes the received data.

At block 806, the model training service generates updates to one ormore local models based on the analysis of the received data, whereinthe one or more updates are configured to update the one or more localmodels at the respective edge devices. In embodiments, the modeltraining service generates the one or more updates to one or morerespective local models at the respective edge devices based on a stateof the one or more respective edge devices and/or a state of the remotenetwork. The state of an edge device may include one or more performanceand/or hardware specifications of the edge device, (e.g., amount of freememory, processor speed, etc.), a health status of the edge device, orany other suitable parameters for representing state of an edge device.In some embodiments, the edge device or other device may provide one ormore configuration files to the model training service that describe thestate of the edge device. The state of the remote network may beavailable bandwidth between any of the computing devices of the network,the state of one or more of the computing devices of the network, or anyother suitable parameters for representing state of a network. At block808, the model training service deploys the one or more updates to theremote network and/or to the respective edge devices.

FIG. 8B is a flow diagram that illustrates updating a local model of anedge device, according to some embodiments. At block 810, an edge device(e.g., model trainer) receives data from a data collector. Inembodiments, at least some of the data received by the edge device maybe global data from one or more other edge devices at other locations.For example a provider network may collect the global data and send itto the edge device. At block 812, the edge device analyzes the receiveddata. At block 814, the edge device generates an update to a local modelbased on the analyses of the received data, wherein the update isconfigured to update the local model of the edge device.

At block 816, the edge device applies the update to the local model. Atblock 818, the edge device sends the data to a model training service ofa remote provider network. In embodiments, the edge device may send thedata to the model training service and receive the data from the datacollector at approximately the same time and/or concurrently. At block820, the edge receives another local model update to the local modelfrom the model training service, wherein the other local model update isbased on the data and/or global data from one or more other edge devicesat other locations. At block 822, the edge device applies the otherlocal model update to the local model. In embodiments, the other updatemay include one or more changes (e.g., deltas or gradient values) to theupdate that was generated by the edge device.

FIG. 9 illustrates a system for updating models for edge devices by ahub device, according to some embodiments. In the depicted embodiment,the edge devices 600 are connected to a local network 104 and include alocal model 602 and a data collector 122. The hub device 100 isconnected to the local network 104 and includes a model trainer 902 thatgenerates one or more model updates 606.

One or more of the edge devices may collect data from a respective datacollector 122 and send the data to the hub device 100 via the localnetwork 104. For example, the edge device 600 a may collect and send thedata 608 a to the hub device 100 and the edge device 600 n may collectand send the data 608 n to the hub device 100.

In embodiments, the model trainer 902 of the hub device 100 may receivethe data 608 from one or more of the edge devices 600. For example, themodel trainer 902 may receive the data 608 a and data 608 n. The modeltrainer 902 may then analyze the received data. The model trainer 902may then generate a local model update 606 a to the local model 602 abased on the analysis of the data 608 a and generate a local modelupdate 606 n to the local model 602 n based on the analysis of the data608 n.

In embodiments, the local model update 606 a is configured to update thelocal model 602 a and the local model update 606 n is configured toupdate the local model 602 n. For example, each of the local modelupdates 606 a, 606 n may include computer-executable instructions and/orother data that is configured be loaded into memory and to update thecorresponding local models 602 a, 602 n when executed by a processor ofthe respective edge device 600 a, 600 n.

In response to the generating of the local model updates 606 a, 606 n,the model trainer 902 may deploy the local model updates 606 a, 606 n tothe respective edge device 600 a, 600 n. Although the above exampleincludes only two model updates, any other number of local model updates606 may be generated and deployed by the model trainer 902 to respectiveedge devices 600 based on received data, in the manner described above.

In some embodiments, the data received by the model trainer may be sentto the provider network 102 for analysis and generation of local modelupdates 606. The model trainer may then receive one or more local modelupdates 606 from the provider network 102. The local model updates 606may then be deployed to one or more respective edge devices 600. Thus,in embodiments, there may be multiple tiers of model trainers and/ormodel training services. At each higher tier, the model trainer and/ormodel training service may be larger, may access a larger training dataset, and may generate local model updates and/or local models configuredto provide a higher level of accuracy and confidence for results thanthe previous tier or other lower tiers.

In embodiments, one or more of the model updates may include one or morenew versions of the respective local models configured to replace therespective local models. Thus, instead of just modifying an existinglocal model, in some cases it is replaced by a different model that is amore recent version.

In some embodiments, the model trainer may initially create and deploy agiven local model to an edge device. For example, the model trainer maygenerate one or more of the local models for one or more of therespective edge devices and then deploy the one or more local models tothe respective edge devices. In embodiments, the model trainer maygenerate a given local model based on topology data or any other datareceived from a corresponding edge device that will be implementing thelocal model.

In embodiments, the model trainer may receive global data from one ormore other remote networks (e.g., collected by one or more other edgedevices of the other networks), analyze the global data, and generatethe one or more updates to local models based on the analysis of thereceived data and the analysis of the global data. The updates may thenbe deployed to respective edge devices. For example, images captured bycameras from many different homes and/or businesses may be used toenhance training data that is used for generation of local models.

In some embodiments, the model trainer may apply different weights to atleast some of the received data and at least some of the global dataduring the analysis of the received data and the analysis of the globaldata. For example, a higher weight may be applied to the received datafrom the local network than to the global data during the analysis ofthe received data and the analysis of the global data. Thus, an amountof data received by the model trainer service from the local network mayhave a larger impact on the training of a local model than the sameamount of global data received by the model trainer.

Conversely, in some embodiments, a higher weight may be applied to theglobal data than to the received data from the local network during theanalysis of the received data and the analysis of the global data. Thus,an amount of global data received by the model trainer may have a largerimpact on the training of a local model than the same amount of datareceived by the model trainer from the local network.

In embodiments, analysis of data from two or more different edge devicesmay be used to generate an update for a local model of a particular edgedevice. Thus, one local model of a particular edge device may be updatedand/or trained not only based on data from the particular edge device,but also based on data from one or more other edge devices of the samelocal network and/or other networks. In other words, the generating ofat least one of the updates to a particular local data processing modelmay be based on analysis of a portion of the data received by the modeltrainer from two or more different edge devices.

In some embodiments, the model trainer may generate and deploy one ormore additional updates for one or more of the respective local dataprocessing models to one or more respective edge devices on a periodicbasis and/or in response to a triggering event. For example, updates maybe generated based on received data and deployed to edge devices on anhourly, daily, or weekly basis, or in response to the model trainerreceiving a command to update one or more of the local models ofrespective edge devices.

FIG. 10 is a flow diagram that illustrates updating models for edgedevices by a hub device, according to some embodiments. At block 1002, amodel trainer of the hub device receives data from one or morerespective edge devices of a local network. At block 1004, the modeltrainer analyzes the received data.

At block 1006, the model trainer generates updates to one or more localmodels based on the analysis of the received data, wherein the one ormore updates are configured to update the one or more local models atthe respective edge devices. At block 1008, the model trainer deploysthe one or more updates to the respective edge devices.

In embodiments, the model training service of the provider networkand/or the model trainer may obtain device configuration files (e.g.,from an edge device or other source) that may be used to optimize and/orgenerate the local model updates that are sent to the edge device andapplied to the local model to update the local model. For example, thedevice configuration files may include one or more parameters thatindicate one or more software and/or hardware configurations of the edgedevice (e.g., size of memory, processor speed, etc.).

In some embodiments, the model training service of the provider networkand/or the model trainer may obtain one or more indications of the stateof one or more edge devices. The indications may be used to optimizeand/or generate respective local model updates that are sent to the edgedevice and applied to the local model to update the local model. Forexample, the indications may include reliability of a connection for anedge device, an amount of free memory available at an edge device, anamount of non-volatile storage available at an edge device, a health ofan edge device (e.g., with respect to a previous health state or withrespect to other edge devices of the local network), and any othersuitable indication of state of an edge device, where the state mayaffect how the local model update is optimized and/or generated.Further, in some embodiments, a model of the model training serviceand/or the provider network may be generated, optimized, and/or modifiedbased on device configuration files for edge devices and/or based onindications of the state of one or more edge devices.

FIG. 11 is a block diagram of an edge device that implements multiplelocal models, according to some embodiments. In the depicted embodiment,the edge device 600 includes a local model manager 1102 that determineswhich one of the local models 602 a-602 n will be used at any given timeto process data received from one or more data collectors 122.

In embodiments, the edge device may receive updates to one or more ofits local models 602 and/or may receive the local models 602 as deployedmodels in the same way or similar way as described for the figuresabove. For example, a model training service of a provider networkand/or a model trainer of a hub device may receive data from the edgedevice 600, analyze the received data, generate one or more updates forone or more of the respective local models 602 based on the analysis,and deploy the one or more updates to the one or more of the respectivelocal models 602.

In some embodiments, the local models 602 are different from each other(e.g., perform one or more different operations than each other forgiven input data) and the different local models 602 are configured toprocess different data received at different times by the at least oneedge device. Thus, the local model manager 1102 may assign particulartime periods to each of the local models to process received data. Forexample, the local model 602 a may be selected by the local modelmanager 1102 to process data received by the data collector 122 duringmorning (am) hours, and the local model 602 n may be selected by thelocal model manager 1102 to process data received by the data collector122 during evening (pm) hours.

In embodiments, the local model manager 1102 may select different onesof the local models 122 in response to detecting different environmentalconditions. For example, when the local model manager 1102 determinesthat an amount of sunlight detected by the data collector 122 is above athreshold level, then the local model manager 1102 may select the model602 a to process data received from the data collector 122. Conversely,when the local model manager 1102 determines that an amount of sunlightdetected by the data collector 122 is at or below the threshold level,then the local model manager 1102 may select the model 602 n to processdata received from the data collector 122.

In various embodiments, different models 602 may have differentarchitectures and be trained using different machine learning techniquesand/or data sets. For example, a model training service of a providernetwork, a model trainer of a hub device, and/or a local model trainerof the edge device may use ensemble methods to use different machinelearning algorithms to generate different models (e.g., neural network,deep neural network, memory network, etc.). By using two or more modelsto process the same collected data, a consensus of the results frommultiple models (e.g., majority or quorum) may be used to select aresult/prediction.

In embodiments, boosting and/or stacking may be used to take advantageof the multiple different models on an edge device. For example,boosting may include generating multiple models by training each newmodel to emphasize training instances that previous modelsmisclassified. Stacking may include combining information from multiplepredictive models to generate a new model (e.g., a stacked model orsecond-level model). In some embodiments, a stacked model outperformseach of the individual models due to its ability to use or give moreweight to results for each base model where it performs best (e.g.,makes more accurate predictions than the other models) and/or itsability to discredit or use less weight for results for each base modelthat performs worse (e.g., makes less accurate predictions than othermodels). In embodiments, the more different the base models are fromeach other, the more accurate results a stacked model may provide.

FIG. 12 is a flow diagram that illustrates selecting a local model toprocess data at an edge device, according to some embodiments. At block1202, the model training service of a provider network and/or the modeltrainer of a hub device deploys two or more local models to an edgedevice of a local network. At block 1204, the edge device receives data.For example, the edge device may receive data from one or more sensorsof the edge device (e.g., environmental sensors) and/or from one or moreother edge devices of the local network.

At block 1206, the local model manager selects one of the local modelsof the edge device to process the data based at least on a current time.In some embodiments, the local model manager selects one of the localmodels of the edge device to process the data based at least on anenvironmental condition (e.g., environmental data collected by asensor). In some embodiments, the local model manager selects one of thelocal models of the edge device to process the data based at least on astate of the edge device. For example, if the remaining useful life of abattery of the edge device is below a threshold amount of time, then thelocal model manager may select one of the models that consumes the leastamount of power to process data, such as switching from a model thatrequires a GPU of the edge device to execute to one that only requires aCPU of the edge device to execute. At block 1208, the edge deviceprocesses the data using the selected local model.

FIG. 13 illustrates a system for implementing model tiering acrossmultiple devices of a network, according to some embodiments. In thedepicted embodiment, the local network includes edge devices 1300 andtier devices 1302. As shown, a given edge device 1300 or tier device1302 includes a model 1304. The model 1304 may be operate in the same orsimilar way as described above for the local model 108. Thus, the model1304 may include software and/or hardware that performs one or moreoperations on received data. In embodiments, the one or more operationsmay include analyzing the data, modifying the data based on theanalyzing, and/or generating a result based on the analyzing (e.g.,prediction, new data, one or more commands, or some other result). Forexample, the result may be a local prediction. In some embodiments, thelocal prediction may be received by a tier manager 1306.

In some embodiments, the tier device 1302 a (e.g., the tier manager 1306a) may transmit the data received from one or more tier devices 1302b-1302 n to the provider network 102. The data may be transmitted via awide-area network 114 (e.g., the internet). Thus, the provider network102 may be considered a remote provider network and may be in anotherphysical location than the hub devices, such as another city, state, orcountry. In embodiments, the provider network 102 includes a tierservice 1308 and a model 1310 that receives the data from the tierdevice 1302 a and processes the received data.

In some embodiments, the tier device 1302 a behaves in a way similar toa hub device as described above. In other embodiments, tier device 1302a may actually include multiple different tier devices 1302 that eachmay communicate with the provider network. Thus, any level of redundancyor any other topology scheme of tier and edge devices and/or number oftier and edge devices may be implemented, in embodiments. For example,the tier devices and/or edge devices may be arranged in a networktopology including one or more of a tree topology, mesh topology, startopology, fully connected topology, partially connected topology, bustopology, ring topology, line topology, point-to-point topology, daisychain topology, and hybrid topology.

In embodiments, the edge devices 1300 include data collectors 122. Anedge device may collect data 1312 via the data collector. In someembodiments, the tier manager 1306 may receive and/or analyze the data1312. The tier manager 1306 may determine whether to process the data1312 using a model 1304 of the edge device or to send the data 1312 toone or more tier devices 1302 of the network 104. Depending on thedetermination, the tier manager 1306 either processes the data 1312using the model 1304 or sends the data 1312 to a tier device 1302.

In embodiments, the tier device 1302 receives the data 1312 from theedge device 1302 and the tier manager 1306 of the tier device 1302determines whether to process the data using a higher tier model 1304 ofthe tier device 1302. In some embodiments, the tier manager 1306 of thetier device 1302 receives and/or analyzes the data 1312. In embodiments,the higher tier model 1304 is larger than the model 1304 of the edgedevice 1300 and/or generates a result (e.g., prediction) with a higherconfidence level than the model 1304 of the edge device 1300 for givendata 1312. In embodiments, the tier device has a larger memory and/orfaster processor.

In response to determining to process the data using the higher tiermodel 1304, the tier manager 1306 of the tier device 1302 processes thedata using the higher tier model 1304, generates a result 1314 based onthe processing, and sends the result 1314 to the edge device 1300 and/oranother endpoint. In embodiments, the edge device 1300 performs andaction and/or generates a command in response to receiving the result1314.

In response to determining not to process the data 1312 using the highertier model 1304, the tier manager 1306 of the tier device 1302 sends thedata 1312 to another tier device 1302 (or to the provider network 102),where the data is processed by another model to generate a result 1314.In embodiments, the tier device 1302 then receives the result 1314 fromthe other tier device 1302 (or from the provider network 102), whereinthe result is based on the data 1312. The tier device 1302 may then sendthe result 1314 to the edge device 1300 and/or another endpoint.

In embodiments, the edge device (e.g., the tier manager of the edgedevice) may determine, based on criteria, whether to process the datausing the model of the edge device or to send the data to the tierdevice. The criteria may include one or more of an amount of energyrequired for the model to process the data, an amount of time requiredfor the model to process the data, an amount of time since the model wasupdated, an amount of bandwidth available to send the data to the tierdevice (including, in some cases, no bandwidth), and a reliability ofthe network connection between the edge device and one or more otherdevices of the local network (e.g., amount of time there is aconnectivity vs. no connectivity).

For example, the tier manager of the edge device may determine that anamount of time and/or energy required for the model to process the datais above a threshold amount and in response, send the data to the tierdevice. Conversely, the tier manager of the edge device may determinethat the amount of time and/or energy required for the model to processthe data is not above a threshold amount and in response, process thedata using the model of the edge device.

In embodiments, the tier manager may determine that an amount of timesince the model was updated is above a threshold amount and in response,send the data to the tier device. Conversely, the tier manager of theedge device may determine that that the amount of time since the modelwas updated is not above the threshold amount and in response, processthe data using the model of the edge device.

In some embodiments, the tier manager may determine that an amount ofbandwidth available to send the data to the tier device is above athreshold value and in response, send the data to the tier device.Conversely, the tier manager of the edge device may determine that thatthe amount of bandwidth available to send the data to the tier device isnot above a threshold value and in response, process the data using themodel of the edge device.

In embodiments, the above criteria may be also be used by a tier managerof a tier device to determine whether to process the data using themodel of the tier device or to send the data to another tier device (orto the provider network). Thus, in embodiments, the data may bypropagated through one or more tier devices until a particular tierdevice processes the data. After the particular tier device processesthe data, the result from the processing may be propagated back to theedge device and/or to one or more other endpoints.

In some embodiments, an edge device or tier device may generate aprediction based on processing of the data 1312 using the model of theedge device or the tier device. The tier manager may then determinewhether a confidence level of the prediction is below a thresholdconfidence level. If so, then the tier manager may send the data to atier device (or another tier device) for processing by a model of thetier device (or other tier device). If not, then the edge device or thetier device may use the prediction (e.g., to generate a response),without sending the data on to the tier device (or to the other tierdevice).

Thus, in embodiments, data may be propagated to one or more tier devicesuntil the confidence level for the prediction is not below the thresholdlevel. At that point, the prediction may be propagated back to the edgedevice and/or to one or more other endpoints. In embodiments, theprovider network may include the highest tier model. Therefore, thefurthest that data can propagate may be to the model 1310 of theprovider network. Thus, in embodiments, the tier service 1308 makesdeterminations as described above for the tier manager 1306.

FIG. 14 is a block diagram of an edge device that implements a model anda tier manager, according to some embodiments. In the depictedembodiment, the edge device 1300 includes processor 200, a memory 202, abattery 204, a network interface 206, and one or more data collectors122. The memory 202 includes a tier manager 1306 and a model 1304. Inembodiments, a tier device 1302 may include some or all of thecomponents described for the edge device 1300.

In some embodiments, the memory 202 includes executable instructions andthe processor 208 executes the instructions in order to implement thetier manager 1306 and/or the model 1304. In embodiments, the networkinterface 206 communicatively couples the edge device 1300 to the localnetwork. Thus, the edge device 1300 may transmit data to and receivedata from tier devices via the network interface 206. In embodiments,the network interface 206 may transmit data via a wired or wirelessinterface.

In some embodiments, the edge device and one or more of its components(e.g., processor and memory) may be relatively lightweight and smallercompared to components (e.g., processor and memory) used by the tierdevices or the provider network to implement the models. For example,the size of one or more memories and/or one or more processors used bythe tier devices or servers of the provider network may be at least anorder of magnitude larger than the size of the memory and/or theprocessor used by the edge device 1300.

Therefore, in embodiments, the models of tier devices or the providernetwork may produce more accurate results (e.g., predictions) withhigher confidence levels, and at a faster rate than the model of theedge devices. In embodiments, the higher up a tier device is in thehierarchy of tier devices, the larger the model is. Thus, each step upin level may provide a more accurate result with higher confidencelevels, and/or at a faster rate than one or more lower levels.

FIG. 15 is a flow diagram that illustrates implementing model tieringacross multiple devices of a network, according to some embodiments. Atblock 1502, a data collector collects data at an edge device. At block1504, the tier manager determines whether to process the data using amodel of the edge device or to send the data a tier device connected tothe network. If the tier manager determines to process the data at theedge device, then at block 1506, the edge device processes the datausing the model of the edge device. Then the model generates a result atblock 1514.

If the tier manager determines to not process the data at the edgedevice, then at block 1508, the edge device sends the data to the tierdevice. In embodiments, the tier device may be located at a bottom levelof a hierarchy of tier devices. At block 1510, the tier manager of thetier device determines whether to process the data using a model of thetier device or to send the data to another tier device connected to thenetwork. In embodiments, the other tier device may be located at thenext level up from the current tier device. If the tier managerdetermines to send the data to the other tier device, then the processreturns to block 1508.

If the tier manager determines to process the data using a model of thetier device, then at block 1512, the tier device processes the datausing the model of the tier device. Then the model of the tier devicegenerates a result at block 1514. At block 1516, the tier device sendsthe result to an endpoint. For example, the tier device may send theresult back to the tier device or edge device that it received the datafrom and/or the tier device may send the result to one or more otherendpoints.

FIG. 16 is a flow diagram that illustrates implementing model tieringacross multiple devices of a network to generate a prediction, accordingto some embodiments. At block 1602, a data collector collects data at anedge device. At block 1604, model of the edge device processes the data.In embodiments, the tier manager determines to process the data usingthe model of the edge device and in response, the model processes thedata.

At block 1606, the model generates a prediction. At block 1504, the tiermanager determines whether a confidence level for the prediction isbelow a threshold level. If the tier manager determines that theconfidence level is not below the threshold level, then at block 1610,the tier manager generates a response based on the prediction. Theprocess then returns to block 1602.

If the tier manager determines that the confidence level is below thethreshold level, then at block 1612, the tier manager sends the data toa tier device. At block 1614, the tier device processes the data using amodel at the tier device. In embodiments, a tier manager of the tierdevice determines to process the data using the model of the tier deviceand in response, the model of the tier device processes the data.

At block 1616, the model of the tier device generates a prediction. Atblock 1618, the tier manager of the tier device determines whether aconfidence level for the prediction is below the threshold level. If thetier manager determines that the confidence level is not below thethreshold level, then at block 1620, the tier manager generates aresponse based on the prediction. The process then returns to block1602.

At block 1616, if the tier manager determines that the confidence levelis below the threshold level, then the process returns to block 1612,where the tier manager sends the data to another tier device. Forexample, the tier device may be a part of a hierarchy of tier devices asdescribed above. Thus, the tier manager may send the data to the nexttier device in the hierarchy. As described above, this process maycontinue until a prediction is generated with a confidence level that isnot below the threshold level (e.g., a minimum confidence level).

FIG. 17 illustrates a system for implementing model tiering by sendingdifferent portions of data to different models, according to someembodiments. In the depicted embodiment, a local network includes edgedevices 1300 and tier devices 1302. As shown, the tier devices may bearranged in a hierarchy of different tiers. In embodiments, the localnetwork may be arranged in one or more of the topologies discussed abovefor FIG. 13.

In embodiments, one or more of the tiers may include other devicesoutside of the local network, such as a base station 1702 and a providernetwork 102. For example, in embodiments, the base station 1702 mayinclude one or more tier managers 1306 operating on one or morerespective tier devices 1302 that include respective models 1304. Thebase station 1302 may communicate with the provider network 102 and atleast some of the tier devices 1302 of the local network via a wide-areanetwork 114 (e.g., the internet).

As discussed above, the model 1304 may include software and/or hardwarethat performs one or more operations on received data. In embodiments,the one or more operations may include analyzing the data, modifying thedata based on the analyzing, and/or generating a result based on theanalyzing (e.g., prediction, new data, one or more commands, or someother result). For example, the result may be a local prediction. Insome embodiments, the local prediction may be received by a tier manager1306.

In embodiments, there may be one or more edge devices that each includea tier manager, at least one model, and at least one data collector.Each level of tier devices may include one or more tier devices thateach include a tier manager and at least one model. In embodiments, anynumber of levels of edge and/or tier devices may exist.

In embodiments, an edge device 1300 may collect data via one or moredata collectors. In some embodiments, the collected data may include twoor more different portions of data 1704. The tier manager 1306 mayreceive and/or analyze the data 1312. In some embodiments, the tiermanager 1306 may split the received data into the two or more differentportions of data 1704. For example, image data captured from a cameramay be de-muxed into red image data, green image data, and blue imagedata. As another example, a portion of the data may be data collectedfrom one sensor (e.g., audio data from a microphone) and another portionof the data may be image data collected from another sensor (e.g., acamera).

The tier manager 1306 may determine whether to process the portions ofdata 1704 at one or more respective models 1304 of the edge devices orto send the portions of data 1704 to one or more respective tier devices1302. Depending on the determination, the tier manager 1306 eitherprocesses the portions of data 1704 using the respective models 1304 orsends the portions of data 1704 to the respective tier devices at thefirst or bottom level of tier devices 1302. In some embodiments, theportions of data 1704 are generated by different models of respectiveedge devices 1300. Thus, in some embodiments, the above determinationsmay be made by multiple tier managers of the respective edge devices(e.g., using suitable techniques of distributed computing/processing,majority consensus, etc.)).

In some embodiments, the tier manager 1306 may determine to process theportions of data 1704 at one or more respective models 1304 of the edgedevices and send the processed portions of data 1704 to one or morerespective tier devices 1302. Thus, the portions of data 1704 sent tothe one or more respective tier devices 1302 may be data collected bythe data collectors 122 that has been modified and/or transformed by theone or more respective models 1304.

In embodiments, the respective tier devices 1302 receive the portions ofdata 1704 from the respective edge devices 1302 and the tier managers1306 of the respective tier devices 1302 determine whether to processthe portions of data 1704 using respective higher tier models 1304 ofthe tier devices 1302. In some embodiments, the tier managers 1306 ofthe tier devices 1302 receive and/or analyze the portions of data 1704to make the above determination.

In embodiments, the higher tier models 1304 are larger than the models1304 of the edge devices 1300 and/or generate one or more results (e.g.,prediction) with a higher confidence level than the model 1304 of theedge device 1300 for given portions of data 1704. In embodiments, thetier devices have a larger memory and/or faster compute hardware. Insome embodiments, only one tier manager makes the above determinationsinstead of multiple tier managers. In some embodiments, the respectivetier managers collectively make the above determinations (e.g., usingsuitable techniques of distributed computing/processing, majorityconsensus, etc.).

In response to determining to process the portions of data 1704 usingthe higher tier models 1304, the tier managers 1306 of the tier devices1302 process the portions of data 1704 using the higher tier models1304, generate results based on the processing, and send the results tothe edge devices 1300 and/or another endpoint (e.g., one or more tierdevices 1302 of the next tier). For example, the tier managers 1306 ofthe tier devices 1302 at any given level may use respective models 1304to modify and/or transform the data received from a lower tier device1302 and send the modified/transformed data 1706, 1708, 1710, to thenext level of one or more tier devices 1302 or the tier service 1308. Inembodiments, the one or more edge devices 1300 perform an action and/orgenerates a command in response to receiving the results. Moreover, insome embodiments, the results are consolidated into one result or asmaller number of results.

In some embodiments, in response to determining not to process theportions of data 1704, 1706, 1708 using the higher tier model 1304, thetier manager 1306 of the tier device 1302 may send the portions of data1704 to another level of tier devices 1302 (or to the provider network102), where the portions of data 1704, 1706, 1708, 1710 are processed byother models to generate one or more results. In embodiments, the tierdevices 1302 then receive the one or more results from the other tierdevices 1302 (or from the provider network 102), wherein the one or moreresults are based on the portions of data 1704, 1706, 1708, 1710. Thetier devices 1302 may then send the one or more results to the edgedevices 1300 and/or one or more other endpoints. For example, one imageprocessing model of a particular tier device may process sensor datafrom a red light spectrum, another image processing model of anothertier device may process sensor data from a green light spectrum, andanother image processing model of another tier device may process sensordata from blue light spectrum.

As discussed above for an individual edge device, the edge devices(e.g., the tier managers of the edge devices) may determine, based oncriteria, whether to process the portions of data 1704 using the modelsof the edge devices or to send the portions of data 1704 to the tierdevices. As discussed above, the criteria may include one or more of anamount of energy required for the model to process the portions of data1704, an amount of time required for the model to process the portionsof data 1704, an amount of time since the model was updated, and anamount of bandwidth available to send the portions of data 1704 to thetier devices.

For example, the one or more tier managers of the one or more edgedevices may determine that an amount of time and/or energy required forthe model to process the portions of data 1704 is above a thresholdamount and in response, send the portions of data 1704 to the tierdevices. Conversely, one or more tier managers of the one or more edgedevices may determine that the amount of time and/or energy required forthe model to process the portions of data 1704 is not above a thresholdamount and in response, process the portions of data 1704 using themodel of the edge device.

In embodiments, the one or more tier managers may determine that anamount of time since the models were updated is above a threshold amountand in response, send the portions of data 1704 to the tier devices.Conversely, the one or more tier managers of the edge devices maydetermine that that the amount of time since the models were updated isnot above the threshold amount and in response, process the portions ofdata 1704 using the models of the edge devices.

In some embodiments, the one or more tier managers may determine that anamount of bandwidth available to send the portions of data 1704 to thetier device is above a threshold value and in response, send theportions of data 1704 to the tier device. Conversely, the one or moretier managers of the edge devices may determine that that the amount ofbandwidth available to send the portions of data 1704 to the tierdevices is not above a threshold value and in response, process theportions of data 1704 using the models of the edge devices.

In embodiments, the above criteria may be also be used by one or moretier managers of the tier devices to determine whether to process theportions of data 1704, 1706, 1708 using the models of the tier devicesor to send the portions of data 1704, 1706, 1708, 1710 to other tierdevices of another tier (or to the provider network). Thus, inembodiments, the portions of data 1704, 1706, 1708, 1710 may bypropagated through one or more tiers of tier devices (asmodified/transformed data or as unmodified data) until a particulargroup of tier devices (or provider network) processes the portions ofdata 1704 (or finishes modifying and/or transforming the portions ofdata 1704, 1706, 1708, 1710). After the tier devices process theportions of data 1704 or finishes modifying and/or transforming theportions of data 1704, 1706, 1708, 1710), one or more results from theprocessing may be propagated back to the edge device and/or to one ormore other endpoints.

As discussed above for an individual edge device or tier device, edgedevices or tier devices may generate a prediction based on processing ofthe portions of data 1704, 1706, 1708, 1710. One or more tier managersmay then determine whether a confidence level of the prediction is belowa threshold confidence level. If so, then the one or more tier managersmay send the data to tier devices (or next level of tier devices) forprocessing by models of the tier devices (or next level of tierdevices). If not, then the edge devices or the tier devices may use theprediction (e.g., to generate one or more responses), without sendingthe portions of data 1704, 1706, 1708 on to the tier devices (or to thenext level of tier devices).

Thus, in embodiments, portions of data 1704, 1706, 1708, 1710 may bepropagated to one or more tier devices until the confidence level forthe prediction is not below the threshold level. At that point, theprediction may be propagated back to the one or more of the edge devicesand/or to one or more other endpoints. In embodiments, the providernetwork may include the highest tier model. Therefore, the furthest thatportions of data 1704, 1706, 1708, 1710 can propagate may be to themodel 1310 of the provider network. Thus, in embodiments, the tierservice 1308 makes determinations as described above for the tiermanagers 1306.

FIG. 18 is a flow diagram that illustrates implementing model tiering bysending different portions of data to different models, according tosome embodiments. At block 1802, a one or more data collectors collectdata at an edge device. As described above, the data may be collected bydifferent edge devices.

At block 1804, the one or more edge devices send respective portions ofthe data to respective tier devices. At block 1806, the tier devicesprocess the respective portions of the data at respective models of thetier devices. As described above, in some embodiments, the portions ofdata may be propagated to one or more additional levels of tier devicesbefore being returned to the tier devices. At block 1808, the one ormore edge devices receive one or more results from the tier devices. Atblock 1810, the one or more edge devices generate a response (ormultiple responses) based on the one or more results.

In some embodiments, any of the models described in FIGS. 1-18 mayoperate within the context of a reinforcement learning process fortraining/modifying the models. For example, the provider network and/orhub device may obtain topology data from the local network at multiplepoints in time (e.g., on a periodic basis) and based on the topologydata, periodically modify or replace models to improve accuracy of themodels, improve confidence levels of the results (e.g. predictions)generated by the models, and/or to improve performance of the localnetwork.

In embodiments, the reinforcement learning process is used to obtain aminimum level of confidence for predictions while minimizing one or morecosts associated with obtaining the predictions. For example, the costdue to network traffic/latency and/or power consumption by edge devicesmay be minimized, while still obtaining a minimum level of accuracy. Inembodiments, a level of confidence and/or a level of accuracy may bemeasured in terms of a percentage (e.g., 99% or 90.5%) or any othervalue suitable for quantifying level of confidence or accuracy, from noconfidence or accuracy (e.g., 0%) to full confidence or accuracy (e.g.,100%).

In some embodiments, any of the models of the hub device, edge devices,tier devices, or provider network described in FIGS. 1-18 may operatewithin the context of an event-driven execution environment. Forexample, one or more functions of the model may be assigned torespective events, such that a particular function is triggered inresponse to detection, by the event-driven execution environment, of anevent assigned to the particular function (e.g., receiving data from oneor more particular edge devices). In embodiments, the function mayinclude one or more operations to process the received data, and maygenerate a result (e.g., prediction).

Any of various computer systems may be configured to implement processesassociated with the provider network, base station, hub devices, edgedevices, tier devices, or any other component of the above figures. Forexample, FIG. 19 is a block diagram illustrating an example computersystem that implements some or all of the techniques described herein,according to some embodiments. In various embodiments, the providernetwork, base station, hub devices, edge devices, tier devices, or anyother component of any of FIGS. 1-18 may each include one or morecomputer systems 1900 such as that illustrated in FIG. 19. Inembodiments, the provider network, base station, hub devices, edgedevices, tier devices, or any other component may include one or morecomponents of the computer system 1900 that function in a same orsimilar way as described for the computer system 1900.

In the illustrated embodiment, computer system 1900 includes one or moreprocessors 1910 coupled to a system memory 1920 via an input/output(I/O) interface 1930. Computer system 1900 further includes a networkinterface 1940 coupled to I/O interface 1930. In some embodiments,computer system 1900 may be illustrative of servers implementingenterprise logic or downloadable application, while in other embodimentsservers may include more, fewer, or different elements than computersystem 1900.

In various embodiments, computer system 1900 may be a uniprocessorsystem including one processor 1910, or a multiprocessor systemincluding several processors 1910 (e.g., two, four, eight, or anothersuitable number). Processors 1910 may be any suitable processors capableof executing instructions. For example, in various embodiments,processors 1910 may be embedded processors implementing any of a varietyof instruction set architectures (ISAs), such as the x106, PowerPC,SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessorsystems, each of processors 1910 may commonly, but not necessarily,implement the same ISA.

System memory 1920 may be configured to store instructions and dataaccessible by processor 1910. In various embodiments, system memory 1920may be implemented using any suitable memory technology, such as staticrandom access memory (SRAM), synchronous dynamic RAM (SDRAM),non-volatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions and data implementingdesired functions, such as those methods and techniques described abovefor the file gateway, object storage system, client devices, or serviceprovider are shown stored within system memory 1920 as programinstructions 1925. In some embodiments, system memory 1920 may includedata 1935 which may be configured as described herein.

In one embodiment, I/O interface 1930 may be configured to coordinateI/O traffic between processor 1910, system memory 1920 and anyperipheral devices in the system, including through network interface1940 or other peripheral interfaces. In some embodiments, I/O interface1930 may perform any necessary protocol, timing or other datatransformations to convert data signals from one component (e.g., systemmemory 1920) into a format suitable for use by another component (e.g.,processor 1910). In some embodiments, I/O interface 1930 may includesupport for devices attached through various types of peripheral buses,such as a variant of the Peripheral Component Interconnect (PCI) busstandard or the Universal Serial Bus (USB) standard, for example. Insome embodiments, the function of I/O interface 1930 may be split intotwo or more separate components, such as a north bridge and a southbridge, for example. Also, in some embodiments, some or all of thefunctionality of I/O interface 1930, such as an interface to systemmemory 1920, may be incorporated directly into processor 1910.

Network interface 1940 may be configured to allow data to be exchangedbetween computer system 1900 and other computer systems 1900 or devicesattached to a network, such as the local network discussed above, awide-area network, or a local network within the provider network, forexample. In particular, network interface 1940 may be configured toallow communication between computer system 1900 and/or various I/Odevices 1950. I/O devices 1950 may include scanning devices, displaydevices, input devices and/or other communication devices, as describedherein. Network interface 1940 may commonly support one or more wirelessnetworking protocols (e.g., Wi-Fi/IEEE 802.11, or another wirelessnetworking standard). However, in various embodiments, network interface1940 may support communication via any suitable wired or wirelessgeneral data networks, such as other types of Ethernet networks, forexample. Additionally, network interface 1940 may support communicationvia telecommunications/telephony networks such as analog voice networksor digital fiber communications networks, via storage area networks suchas Fibre Channel SANs, or via any other suitable type of network and/orprotocol.

In some embodiments, system memory 1920 may be one embodiment of acomputer-accessible medium configured to store program instructions anddata as described above. However, in other embodiments, programinstructions and/or data may be received, sent or stored upon differenttypes of computer-accessible media. Generally speaking, acomputer-accessible medium may include computer-readable storage mediaor memory media such as magnetic or optical media, e.g., disk orDVD/CD-ROM coupled to computer system 1900 via I/O interface 1930. Acomputer-readable storage medium may also include any volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR SDRAM, RDRAM, SRAM,etc.), ROM, etc., that may be included in some embodiments of computersystem 1900 as system memory 1920 or another type of memory. Further, acomputer-accessible medium may include transmission media or signalssuch as electrical, electromagnetic, or digital signals, conveyed via acommunication medium such as a network and/or a wireless link, such asmay be implemented via network interface 1940.

In some embodiments, I/O devices 1950 may be relatively simple or “thin”client devices. For example, I/O devices 1950 may be configured as dumbterminals with display, data entry and communications capabilities, butotherwise little computational functionality. However, in someembodiments, I/O devices 1950 may be computer systems configuredsimilarly to computer system 1900, including one or more processors 1910and various other devices (though in some embodiments, a computer system1900 implementing an I/O device 1950 may have somewhat differentdevices, or different classes of devices).

In various embodiments, I/O devices 1950 (e.g., scanners or displaydevices and other communication devices) may include, but are notlimited to, one or more of: handheld devices, devices worn by orattached to a person, and devices integrated into or mounted on anymobile or fixed equipment, according to various embodiments. I/O devices1950 may further include, but are not limited to, one or more of:personal computer systems, desktop computers, rack-mounted computers,laptop or notebook computers, workstations, network computers, “dumb”terminals (i.e., computer terminals with little or no integratedprocessing ability), Personal Digital Assistants (PDAs), mobile phones,or other handheld devices, proprietary devices, printers, or any otherdevices suitable to communicate with the computer system 1900. Ingeneral, an I/O device 1950 (e.g., cursor control device 1960, keyboard1970, or display(s) 1980 may be any device that can communicate withelements of computing system 1900.

The various methods as illustrated in the figures and described hereinrepresent illustrative embodiments of methods. The methods may beimplemented manually, in software, in hardware, or in a combinationthereof. The order of any method may be changed, and various elementsmay be added, reordered, combined, omitted, modified, etc. For example,in one embodiment, the methods may be implemented by a computer systemthat includes a processor executing program instructions stored on acomputer-readable storage medium coupled to the processor. The programinstructions may be configured to implement the functionality describedherein (e.g., the functionality of the data transfer tool, variousservices, databases, devices and/or other communication devices, etc.).

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description to be regarded in an illustrative rather than arestrictive sense.

Various embodiments may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Generally speaking, acomputer-accessible medium may include storage media or memory mediasuch as magnetic or optical media, e.g., disk or DVD/CD-ROM, volatile ornon-volatile media such as RAM (e.g. SDRAM, DDR, RDRAM, SRAM, etc.),ROM, etc., as well as transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

What is claimed is:
 1. A system, comprising: one or more processors; andone or more memories, wherein the one or more memories have storedthereon instructions, which when executed by the one or more processors,cause the one or more processors to implement a model training serviceof a provider network, wherein the model training service is configuredto: receive data from a plurality of edge devices of a remote network;analyze the received data; generate one or more updates to one or morerespective local data processing models based on the analysis of thereceived data, wherein the one or more updates are configured to updatethe one or more respective local data processing models at a respectiveone or more of the edge devices of the remote network; and deploy theone or more updates to the remote network.
 2. The system as recited inclaim 1, wherein the one or more updates comprises one or more newversions of the respective local data processing models configured toreplace the respective local data processing models.
 3. The system asrecited in claim 1, wherein the generating of the one or more updates toone or more respective local data processing models is based at least onone or more of a state of the one or more respective edge devices and astate of the remote network.
 4. The system as recited in claim 1,wherein the model training service is configured to: receive global datafrom one or more other edge devices of one or more other remotenetworks; analyze the global data; and generate the one or more updatesto local data processing models based on the analysis of the receiveddata and the analysis of the global data.
 5. The system as recited inclaim 1, wherein the one or more updates comprise two or more differentlocal data processing models for at least one of the edge devices,wherein the different local data processing models are configured toprocess different data received at different times by the at least oneedge device.
 6. A method, comprising: performing, by at least one hubdevice connected to a network: receiving data from a plurality of edgedevices of the network; analyzing the received data; generating one ormore updates to one or more respective local data processing modelsbased on the analysis of the received data, wherein the one or moreupdates are configured to update the one or more respective local dataprocessing models at a respective one or more of the edge devices of thenetwork; and deploying the one or more updates to the respective edgedevices of the network.
 7. The method as recited in claim 6, wherein theone or more updates comprises one or more new versions of the respectivelocal data processing models configured to replace the respective localdata processing models.
 8. The method as recited in claim 6, wherein thehub device performs: generating the one or more updates based at leaston one or more of a state of the one or more respective edge devices anda state of the network.
 9. The method as recited in claim 6, wherein thehub device performs: receiving global data from one or more other edgedevices of one or more other remote networks; analyzing the global data;and generating the one or more updates to local data processing modelsbased on the analysis of the received data and the analysis of theglobal data.
 10. The method as recited in claim 6, wherein the hubdevice performs: applying different weights to the received data and theglobal data during the analysis of the received data and the analysis ofthe global data.
 11. The method as recited in claim 6, wherein the hubdevice performs: applying a higher weight to the received data than tothe global data during the analysis of the received data and theanalysis of the global data.
 12. The method as recited in claim 6,wherein the one or more updates comprise two or more different localdata processing models for at least one of the edge devices, wherein thedifferent local data processing models are configured to processdifferent data received at different times by the at least one edgedevice.
 13. The method as recited in claim 6, wherein the hub deviceperforms: generating at least one of the updates to a particular localdata processing model of a particular edge device based on analysis of aportion of the data received from other edge devices of the network. 14.A non-transitory computer-readable storage medium storing programinstructions that, when executed by a computing device, cause thecomputing device to implement: receiving data from a data collector;analyzing the received data; generating an update to a local dataprocessing model of the computing device based on the analysis of thereceived data, wherein the update is configured to update the local dataprocessing model; applying the update to the local data processingmodel; sending the data to a model training service of a remote providernetwork; receiving another update to the local data processing modelfrom the model training service of the remote provider network, whereinthe update is based on the data; and applying the other update to thelocal data processing model.
 15. The non-transitory, computer-readablestorage medium of claim 14, wherein the other update comprises a newversion of the local data processing model, and wherein the programinstructions cause the computing device to further implement: replacingthe local data processing model with the new version of the local dataprocessing model.
 16. The non-transitory, computer-readable storagemedium of claim 14, wherein the program instructions cause the one ormore computing devices to further implement: receiving other data fromthe data collector; and generating the local data processing model basedon other data.
 17. The non-transitory, computer-readable storage mediumof claim 14, wherein the program instructions cause the one or morecomputing devices to further implement: receiving global data from themodel training service, wherein the global data is based data collectedby one or more other edge devices of one or more other remote networks;analyzing the global data; and generating the update to the local dataprocessing model based on the analysis of the global data.
 18. Thenon-transitory, computer-readable storage medium of claim 14, whereinthe other update is further based on one or more of: other datacollected by one or more other edge devices of a local network thatincludes the computing device, or global data collected by one or moreother edge devices of one or more other remote networks.
 19. Thenon-transitory, computer-readable storage medium of claim 14, whereinthe other update comprises one or more changes to the update that wasgenerated by the computing device.
 20. The non-transitory,computer-readable storage medium of claim 14, wherein the programinstructions cause the one or more computing devices to furtherimplement: receiving, on a periodic basis, an additional update to thelocal data processing model from the model training service of theremote provider network, wherein the additional update is based at leaston additional data collected by the data collector; and applying theadditional update.